NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Estimating the Wind Resource 

in Uttarakhand: Comparison of 

Dynamic Downscaling with 

Doppler Lidar Wind 

Measurements

J.K. Lundquist and A. Purkayastha

National Renewable Energy Laboratory

C. St. Martin

University of Colorado, Boulder

R. Newsom

Pacific Northwest National Laboratory

Technical Report

NREL/TP-5000-61103

March 2014
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Estimating the Wind Resource 

in Uttarakhand: Comparison of 

Dynamic Downscaling with 

Doppler Lidar Wind 

Measurements

J.K. Lundquist and A. Purkayastha

National Renewable Energy Laboratory

C. St. Martin

University of Colorado, Boulder

R. Newsom

Pacific Northwest National Laboratory

Prepared under Task No. WFK6.1002

Technical Report

NREL/TP-5000-61103

March 2014
NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Available electronically at http://www.osti.gov/scitech

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
iii

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Nomenclature or List of Acronyms

ARM Atmospheric Radiation Measurement

BAT Define acronyms

DOE U.S. Department of Energy

GVAX Ganges Valley Aerosol Experiment

HPC High performance computing

MAE Mean absolute error

MDE Median absolute error

MF Mobile facility

NREL National Renewable Energy Laboratory

RSME Root mean square error

WRF Weather Research and Forecasting
iv

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Executive Summary

Standard methods of wind resource assessment often rely on extrapolation of winds and use 

coarse computational grids to address the effects of complex terrain on atmospheric flow. 

Further, even if measurements are available in complex terrain, those measurements may not be 

representative of the flow nearby if the terrain differs significantly. Previous estimates of the 

wind resources in Uttarakhand, India, indicated that there were minimal wind resources in this 

region. To explore whether or not complex terrain in the region in fact provides localized areas

of increased wind resource, we employed a dynamic downscaling method using the Weather 

Research and Forecasting model to provide detailed estimates of wind speeds at approximately 

1-km horizontal resolution in the finest nested simulation. The only measurements available in 

the region for verification are from a 9-month Doppler lidar deployment. As this region 

experiences three distinct seasons, one 7–10 day period from each season was selected from the 

period during which Doppler lidar data were available. Hourly estimates of Doppler lidar wind 

speed and wind direction were calculated and compared with simulated winds. The simulations 

generally underestimated the observed wind speed: lidar observations indicate average wind 

speeds of at least 6.3 m/s in each of the periods simulated. Although localized pockets of higher 

wind resource were evident at altitudes of 80–100 m above the surface, the general resource in 

Uttarakhand could be characterized as minimal from the simulations, with average wind speeds 

equal to or below the typical turbine cut-in speed of ~ 3 m/s

-1 or less at 80–100 m above the 

surface. However, the results of these simulations do not constitute a definitive wind resource 

assessment. More extensive observations at other sites within this region and at wind-energyrelevant altitudes are required to validate the simulations’ estimates of the wind resource in 

Uttarakhand. This region could provide a valuable wind resource in proximity to large 

population centers, and the few existing observations suggest some wind resource was not 

captured with the simulations. Future wind resource estimation efforts should include 

deployment of observational platforms (such as lidar or meteorological towers) in several of the 

lower-altitude valleys of Uttarakhand over the course of a year. These observations could then be 

compared with a larger set of simulations similar to those executed for this study to assess the 

ability of Uttarakhand to provide a valuable resource of renewably-generated electricity.
v

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table of Contents

List of Figures ............................................................................................................................................. v

1 Introduction ............................................................................................................................................. 1

2 Data and Methods ................................................................................................................................... 2

2.1 Mesoscale Simulations.................................................................................................................... 2

2.2 Lidar Observations.......................................................................................................................... 5

3 Results ..................................................................................................................................................... 7

3.1 Impact of Horizontal Resolution on Estimation of the Wind Resource ........................................... 8

3.2 Variability of the Wind Resource with Height.............................................................................. 20

3.3 Comparison of Mesoscale Simulations to Lidar Data ................................................................... 23

3.3.1 Winter .................................................................................................................................. 24

3.3.2 Monsoon .............................................................................................................................. 24

3.3.3 Spring................................................................................................................................... 26

3.4 Diurnal Variability of the Wind Resource..................................................................................... 26

3.5 Comparison of Simulations With Existing wind resource assessments for Uttarakhand.............. 26

4 Summary and Conclusions.................................................................................................................. 28

Acknowledgements.............................................................................................................................. 29

References............................................................................................................................................ 30

List of Figures

Figure 1: Map of WRF simulation domains .............................................................................................. 3

Figure 2: Detailed map of WRF simulation domains............................................................................... 4

Figure 3: Percentage of Doppler lidar data return from each of the three seasons ............................ 6

Figure 4: Time series of wind speeds at ~ 80-m elevation measured by the Doppler lidar during the 

November set of simulations. ............................................................................................................. 7

Figure 5: Time series of wind speeds at ~ 80-m elevation measured by the Doppler lidar during the 

August set of simulations.................................................................................................................... 7

Figure 6: Time series of wind speeds at ~ 80-m elevation measured by the Doppler lidar during the 

March set of simulations ..................................................................................................................... 8

Figure 7: Average wind speed at approximately 80-m above the surface from 30-km resolution 

simulations of 1-7 Nov 2011 ................................................................................................................ 9

Figure 8: Average wind speed at approximately 80-m above the surface from 10-km resolution 

simulations of 1-7 Nov 2011 .............................................................................................................. 10

Figure 9: Average wind speed at approximately 80-m above the surface from 3.33-km resolution 

simulations of 1-7 Nov 2011 .............................................................................................................. 11

Figure 10: Average wind speed at approximately 80-m above the surface from 1.1-km resolution 

simulations of 1-7 Nov 2011 .............................................................................................................. 12

Figure 11: Average wind speed at approximately 80-m above the surface from 30-km resolution 

simulations of 8-15 Aug 2011............................................................................................................ 13

Figure 12: Average wind speed at approximately 80-m above the surface from 10-km resolution 

simulations of 8-15 Aug 2011............................................................................................................ 14

Figure 13: Average wind speed at approximately 80-m above the surface from 3.3-km resolution 

simulations of 8-15 Aug 2011............................................................................................................ 15

Figure 14: Average wind speed at approximately 80-m above the surface from 1.1-km resolution 

simulations of 8-15 Aug 2011............................................................................................................ 16

Figure 15: Average wind speed at approximately 80-m above the surface from 30-km resolution 

simulations of 1-8 Mar 2012............................................................................................................... 17

Figure 16: Average wind speed at approximately 80-m above the surface from 10-km resolution 

simulations of 1-8 Mar 2012............................................................................................................... 18

Figure 17: Average wind speed at approximately 80-m above the surface from 3.3-km resolution 

simulations of 1-8 Mar 2012............................................................................................................... 19

Figure 18: Average wind speed at approximately 80-m above the surface from 1.1-km resolution 
vi

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

simulations of 1-8 Mar 2012............................................................................................................... 20

Figure 19: Average wind speed at approximately 100-m above the surface from 1.1-km resolution 

simulations of 1-7 Nov 2011 .............................................................................................................. 21

Figure 20: Average wind speed at approximately 100-m above the surface from 1.1-km resolution 

simulations of 8-15 Aug 2011............................................................................................................ 22

Figure 21: Average wind speed at approximately 100-m above the surface from 1.1-km resolution 

simulations of 1-8 Mar 2012............................................................................................................... 23

Figure 22: Time series of Doppler lidar winds compared with WRF forecasts from all domains 

during November simulation period................................................................................................. 25

Figure 23: Time series of Doppler lidar winds compared with WRF forecasts from all domains 

during August simulation period. ..................................................................................................... 25

Figure 24: Time series of Doppler lidar winds compared with WRF forecasts from all domains 

during March simulation period........................................................................................................ 27

Figure 25: Average diurnal cycle of hub-height winds from lidar observations during the March 

simulation period................................................................................................................................ 27
1

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

1 Introduction

Complex terrain can induce strong wind flows that can produce significant wind resources 

(Whiteman 2000). If peaks and valleys are not represented in simulations, however, the effects of 

terrain on flows cannot emerge in the simulations. Wind resource assessment in complex terrain 

is particularly challenging because of the nature of topographically driven flows (Brower, 2012). 

The speed, direction, and daily cycle of these flows tend to be poorly represented in typical wind 

resource assessment approaches, therefore a refined modeling approach that captures these 

complex flows may be appropriate. Such a modeling approach consists of mesoscale numerical 

weather prediction simulations for a region of interest for a limited time period. These 

simulations are computationally intensive and require validation with field observations.

The Uttarahkand region of northeastern India was chosen as a candidate for exploration of the 

advantages of using high-resolution mesoscale modeling for wind resource assessment because 

of Uttarakhand’s complex terrain and proximity to the population center of New Delhi: 

transmission of renewably generated electricity to this large population center would be 

desirable. Although India as a nation had approximately 20 GW of installed wind capacity as of 

Oct. 31, 2013 (Ministry of New and Renewable Energy, 2013), based on previous wind resource 

assessments (Centre for Wind Technologies (CWET), 2013), Uttarahkand appeared to have 

minimal wind development potential. As summarized by Hossain et al. (2011), wind energy 

resources in India have historically been underestimated. Further, northeastern Uttarakhand 

includes the very complex terrain of the Himalayas, which made this region a robust challenge 

for mesoscale modeling in complex terrain. A recent analysis of detailed meteorological 

observations in a mountain pass in Switzerland (Clifton et al., 2013) suggests that mountainous 

regions may in fact be suitable for wind energy development due to the channeling of flow. 

Finally, a brief period of atmospheric observations in Uttarakhand was available from the 

Ganges Valley Aerosol Experiment (GVAX) sponsored by the U.S. Department of Energy’s 

(DOE’s) Atmospheric Radiation Measurement Program and conducted under an existing Science 

and Technology Cooperation Agreement between the governments of the United States of 

America and the Republic of India (see http://www.arm.gov/campaigns/amf2011gvax for more 

information). These refined observations, which were collected from June 2011 to March 2012, 

included detailed wind profiles in the lower atmospheric boundary layer from a Doppler lidar 

dataset (data provided by the U.S. Department of Energy’s Pacific Northwest National 

Laboratory). 

This report explores the ability of dynamically downscaled simulations to assess wind resources 

in Uttarakhand and compares these simulations to the only boundary-layer wind profile data 

known for the region, the Doppler lidar dataset available from one location in the region. The 

simulations and the lidar observations are described in detail in Section 2. Section 3 explores

simulation results, including the impact of horizontal resolution on the simulations and the

vertical variability of the wind resource. Although meteorological data for only one location 

were available for model validation, agreement between the simulations and the observations 

was also assessed, and a comparison between these simulations and previous wind resources 

assessments in the region is presented. Finally, this report summarizes the diurnal variability in 

the wind resource based on observations.
2

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2 Data and Methods

2.1 Mesoscale Simulations

Numerous modeling studies have indicated the importance of high-resolution topographic 

databases for accurate simulations of winds near the surface and at wind turbine altitudes. 

Regional models, with smaller domains and finer resolutions than global models, can better 

resolve small-scale features (such as terrain variations) that depend on the surface boundary 

(Castro et al., 2005). Therefore, global-scale simulations are typically refined by using a limitedarea model at finer resolution for the domain of interest. Initial conditions and boundary 

conditions are provided by a global model, while a regional climate model adds detail and 

refinement in response to regional scale forcing (e.g., topography, coastlines, and land use/land 

cover data) as the regional model interacts with the larger-scale atmospheric circulations. The 

purpose of downscaling is to obtain high-resolution detail as accurately as possible for the region 

of interest. 

Numerous studies demonstrate the utility of dynamic downscaling. An investigation of more 

than a decade of high-wind events in the Sierra Mountains of California (Hughes et al., 2012) 

demonstrates that 6-km resolution dynamically downscaled simulations are required to achieve 

good agreement with radar wind profiler observations as compared to coarser-scale reanalysis 

data. Simulations of the complex terrain around Greenland (DuVivier and Cassano, 2013) using 

the Weather Research and Forecasting (WRF) model at 10-km horizontal resolution were 

superior to the simulations at coarser resolutions. An investigation of precipitation forecasting in 

the Himalayan region (Dimri et al., 2014) also emphasizes the importance of high-resolution 

simulations. 

For this exploration of the utility of dynamic downscaling of wind resources in Uttarakhand, 

simulations of the atmospheric flow were performed with the WRF numerical weather prediction 

model (Skamarock et al. 2008). Initial and boundary conditions for these simulations came from 

the Global Forecasting System Extended (global) one-half degree archive (Unidata, 2013), which 

provided updates every six hours. Forty vertical levels were used, with 13 levels in the lowest 

300 m of the atmosphere to ensure high resolution of the boundary layer in the complex terrain. 

The four domains were one-way nested by a factor of three, with the outermost domain at 30-km 

resolution, the second at 10-km resolution, the third at 3.33-km resolution, and the fourth at 1.1-

km resolution. Each domain consisted of 100 cells by 100 cells, as shown in Figure 1 and Figure 

2, and the Doppler lidar observation station is located in the center of Domain 4. The time step 

for the outer domain was 10 seconds and was reduced by a factor of three with each consecutive 

nest. Cloud microphysics were parameterized with the Morrison double-moment scheme. 

Cumulus clouds were parameterized with the Kain-Fritsch scheme on the outer three domains. 

Longwave radiation was parameterized with the Rapid Radiative Transfer (RRTM) model, and 

shortwave radiation was parameterized with the Dudhia scheme. The surface layer and the 

planetary boundary layer were parameterized with the Mellor Yamada Nakanishi Niino 

(MYNN2) models (Nakanishi and Niino, 2006). Computational resource limitations required 

execution of only a single deterministic simulation for each of the time periods examined here, 

although an ensemble of simulations exploring the influence of model physics choices would 

provide insight into variability (Mahoney et al., 2012). Alternatively, reliable uncertainty 

quantification could be provided with hybrid statistical-dynamic downscaling techniques such as 
3

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

the analog ensemble presented by Delle Monache et al. (2013), which would require a single 

deterministic simulation to generate an analog ensemble if more observations were available for 

inclusion. 

Figure 1: Map of WRF simulation domains. The spatial resolution of Domains 1, 2, 3, and 4 is 30-, 

10-, 3.3-, and 1.1-km, respectively. The observation station is located in the center of Domain 4.
4

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 2: Detailed map of WRF simulation domains. The observation station is located in the 

center of Domain 4.
5

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Although simulations for an entire year or several years (Brower et al., 2012) or the selection of 

representative days over several years (Rife et al., 2013) would be preferable for a wind resource 

assessment, computational and data comparison constraints required a selection of representative 

seasons from one year. Uttarakhand has three primary seasons: summer (March to June), winter 

(October to February), and monsoon (July to September)1. The availability of lidar data for 

comparison also constrained the choice of observational periods, as data was only available from 

June 2011 to March 2012. The simulations for this analysis were performed for eight days in 

August (monsoon), nine days in October and November (winter), and seven days in March 

(summer). For all three seasons, the complete simulation runs required approximately 2,470 node 

hours.

2.2 Lidar Observations

Conventional wind resource assessment campaigns rely on observations collected throughout a 

region, rather than the limited set of observations from one location available here. The U.S. 

Department of Energy’s Atmospheric Radiation Measurement (ARM) Program’s mobile facility 

(MF) participated in the Ganges Valley Aerosol Experiment (GVAX) that explored the effects of 

atmospheric aerosols in the Ganges Valley of Uttarahand on cloud formation and monsoon 

activity over the Indian Ocean (http://www.arm.gov/campaigns/amf2011gvax). Although most 

of the ARM instrument deployments for GVAX focused on measurements of aerosols and their 

effects on atmospheric radiation, the deployment of a Doppler Lidar (DL) provided detailed 

measurements of winds at one location, the ARIES Observatory in Nainital, Uttarakhand, from 

June 2011 to March 2012. (ARIES is the Aryabhatta Research Institute of ObservationalSciencES, known as ARIES since 2004.) No other measurements of wind at altitudes relevant for 

wind energy were identified.

The ARM MF Doppler lidar (DL) is described in Newsom (2012). As an active remote sensing 

instrument, it provides range- and time-resolved measurements of radial velocity; multiple 

vertical scans can be aggregated together to provide profiles of horizontal winds. The DL

operates in the near-infrared (1.5 microns) and is sensitive to backscatter from micron-sized 

aerosols. Aerosols are ubiquitous in the low troposphere and are ideal tracers of atmospheric 

winds. The DL is capable of measuring wind velocities under clear-sky conditions with very 

good precision (typically ~10 cm/sec). Profiles of the mean winds are derived from hourly planposition-indicator (PPI) scan data using a modified velocity-azimuth display algorithm (Banta et 

al. 2002, Browning et al. 1968).

The lidar data returns for hourly averages of wind speed at altitudes between 40 m and 500 m 

were generally of very good quality(Figure 3) except for the August time period, when heavy 

precipitation influenced the ability of the lidar to collect wind speed measurements. Wind speeds 

at approximately 80 m above the surface exhibited considerable variability (Figure 4-Figure 6) 

with overall maximum wind speeds ~15–18 m/s. Doppler lidar measurements indicated the 

average wind speed for the weeks selected in each season at approximately 80 m above the 

 

1 Traditionally, Indians recognize six seasons or Ritu, each about two months long. The six seasons 

include spring, summer, monsoon, autumn, winter, and prevernal. As Uttarakhand is located in the 

Himalayan foothills, its seasons vary from those of other parts of India.
6

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

surface is from 6.3 m/s (November and August) to 6.6 m/s (March). As expected, mean wind 

speeds are higher at approximately 100 m above the surface: November and August have 

average wind speeds of 6.6 m/s while March shows an average wind speed of 6.7 m/s. The 

number of data points available for consideration (less than 200) are not sufficient for estimating 

the underlying distribution (such as a Weibull distribution).

Figure 3: Percentage of Doppler lidar data return from each of the three seasons
7

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 4: Time series of wind speeds at ~ 80-m elevation measured by the Doppler lidar during the 

November set of simulations.

Figure 5: Time series of wind speeds at ~ 80-m elevation measured by the Doppler lidar during the 

August set of simulations.
8

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 6: Time series of wind speeds at ~ 80-m elevation measured by the Doppler lidar during the 

March set of simulations

3 Results

3.1 Impact of Horizontal Resolution on Wind Resource Estimates

Complex terrain can induce strong wind flows that can produce significant wind resources 

(Whiteman 2000). If peaks and valleys are not represented in simulations, however, the effects of 

terrain on flows cannot emerge in the simulations. Increased horizontal resolution provided by

the downscaled numerical weather prediction model can theoretically identify areas of wind 

resource that would have been overlooked with coarser-scale simulations. A comparison of the 

coarse simulations for November (Figure 7-Figure 9) to the finest simulations for that month 

(Figure 10) revealed that the strong averaged wind resource of the finest domain in the valleys in 

the northwest are overlooked by the coarse simulations which cannot resolve those valleys. The 

finest-scale simulations suggest strong average winds in isolated valleys in northwestern 

Uttarakhand (5 km south of Patti Gujru). It is likely that even finer-scale simulations could 

identify other regions of significant resource.
9

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 7: Average wind speed at approximately 80-m above the surface from 30-km resolution 

simulations of 1-7 Nov 2011
10

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 8: Average wind speed at approximately 80-m above the surface from 10-km resolution 

simulations of 1-7 Nov 2011
11

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 9: Average wind speed at approximately 80-m above the surface from 3.33-km resolution 

simulations of 1-7 Nov 2011
12

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 10: Average wind speed at approximately 80-m above the surface from 1.1-km resolution 

simulations of 1-7 Nov 2011

Simulations in August (Figure 11 - Figure 14) and March (Figure 15 - Figure 18) show similar 

patterns, although the overall wind speeds are very low, typically below the turbine cut-in speed 

of 3.0 m/s.
13

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 11: Average wind speed at approximately 80-m above the surface from 30-km resolution 

simulations of 8-15 Aug 2011
14

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 12: Average wind speed at approximately 80-m above the surface from 10-km resolution 

simulations of 8-15 Aug 2011
15

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 13: Average wind speed at approximately 80-m above the surface from 3.3-km resolution 

simulations of 8-15 Aug 2011
16

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 14: Average wind speed at approximately 80-m above the surface from 1.1-km resolution 

simulations of 8-15 Aug 2011
17

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 15: Average wind speed at approximately 80-m above the surface from 30-km resolution 

simulations of 1-8 Mar 2012
18

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 16: Average wind speed at approximately 80-m above the surface from 10-km resolution 

simulations of 1-8 Mar 2012
19

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 17: Average wind speed at approximately 80-m above the surface from 3.3-km resolution 

simulations of 1-8 Mar 2012
20

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 18: Average wind speed at approximately 80-m above the surface from 1.1-km resolution

simulations of 1-8 Mar 2012

3.2 Variability of the Wind Resource with Height

Although current utility-scale turbines have average hub heights on the order of 80 m, new 

developments in technology are expected to increase hub heights to at least 100 m or more. Wind 

speeds were extracted from these simulations at approximately 100 m above the surface to 

explore whether or not more wind resource was available at higher altitudes due to nocturnal jet 

phenomena (Banta et al., 2002). There was no evidence of a significant increase of averaged 
21

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

wind speed with height in the averaged wind resource plots for the highest resolution simulations 

of each season (Figure 19-Figure 21).

Figure 19: Average wind speed at approximately 100-m above the surface from 1.1-km resolution 

simulations of 1-7 Nov 2011
22

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 20: Average wind speed at approximately 100-m above the surface from 1.1-km resolution 

simulations of 8-15 Aug 2011
23

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 21: Average wind speed at approximately 100-m above the surface from 1.1-km resolution 

simulations of 1-8 Mar 2012

3.3 Comparison of Mesoscale Simulations to Lidar Data

Evaluation of simulations, as compared with observations, can rely on several different metrics, 

typically relying on measurements from multiple locations rather than measurements from one 

location. Several recent surveys of error and verification metrics for wind energy forecasting 

(Geibel et al., 2011; Bielecki et al., 2010, among others) have provided useful summaries of 

performance metrics. Several metrics are commonly presented in assessing wind energy 

forecasts, yet no individual metric offers a complete description of error tendencies. 
24

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Traditionally, mean absolute error (MAE), median absolute error (MDE) and root mean square 

error (RSME) have been widely used and accepted by the forecasting industry as standard 

methods for quantification of forecast error. The MAE provides insight about the average 

magnitude of the errors over an entire dataset; if the forecast error were simply averaged, then 

positive and negative errors would be cancelled out. Landberg and Watson (1994) emphasize 

that the use of the mean error would be inaccurate and would lead to misinterpretation as 

negative and positive errors may be averaged to give a low mean error. However, this advantage 

of the MAE comes at the expense of losing information about whether the forecast consistently 

under-predicts or over-predicts wind speeds. This bias can be important. The MDE behaves 

similarly to the MAE with less sensitivity to outliers. Finally, the Pearson correlation coefficient, 

R, between observed values and forecast values can indicate general patterns of agreement.

As discussed in detail in the following sections, the general agreement is very poor between the 

forecasts and the observations at the one available site. Correlation coefficients between a time 

series of lidar observations and WRF simulations at approximately the same altitude are less than 

0.4 between the simulations and the profiles, implying no correlation between the two and 

underscoring the challenge of numerical weather prediction in such complex terrain. It is 

surmised that some of the localized terrain forcing in the vicinity of the observation site (within a 

mountain valley) is not represented in these simulations. Detailed verification of these forecasts 

would involve multiple observing stations within the simulation domain that preferably measure 

winds at multiple altitudes (Warner, 2011) and consider other meteorological quantities such as 

temperature, humidity, and precipitation. A detailed comparison should also involve comparing 

the wind speed probability distribution functions over a longer period of time. It appears that 

although the timing is off, the simulations do have some skill in simulating the wind speed 

variability (e.g., Fig. 23).

3.3.1 Winter

The lidar measurements from the week of November exhibit a moderate average wind speed at 

hub height of approximately 6.3 m/s. Of the three simulation periods, the November simulations 

exhibited the highest localized wind speeds, generally constrained to a few valleys in the 

northwest of the domain. However, the wind resource is generally simulated, on average, as very 

low, with a mean value in the finest-scale domain of less than 2 m/s(Figure 22). The mean 

absolute error between the lidar observations and the simulations’ predictions at the lidar 

location greatly exceeds the average wind speed predictions. In the finest domain, the MAE 

between the WRF simulations and the lidar observations is 4.9 m/s, the MDE is 4.7 m/s, and the 

RMSE is 5.6 m/s. The WRF simulations clearly failed to capture a mechanism driving the 

stronger winds observed with the lidar.

3.3.2 Monsoon

The lidar measurements from the selected week in August exhibit a moderate average wind 

speed at hub height of approximately 6.3 m/s, similar to those of November. However, the WRF 

simulations suggest much lower average wind speeds in every domain (Figure 23). Only the 

second domain (10-km horizontal resolution) average wind speed exceeds 3 m/s. In the finestscale domain, which was expected to be most accurate, the MAE between the WRF simulations 

and the lidar observations was 3.9 m/s (greater than the average wind speed of 2.9 m/s), the 

MDE 3.5 m/s, and the RMSE 4.8 m/s, suggesting that the simulations have little skill in 
25

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

matching the observations at the lidar location. There may be substantial wind resource, as 

suggested by the observations, but the simulations as designed here cannot represent it.

Figure 22: Time series of Doppler lidar winds compared with WRF forecasts from all domains 

during November simulation period

Figure 23: Time series of Doppler lidar winds compared with WRF forecasts from all domains 

during August simulation period.
26

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3.3.3 Spring

The lidar measurements from the March time period show an average wind speed of 6.7 m/s at 

hub height, slightly higher than the measurements in November and August. Although the 

average wind speed in the finest domain of the WRF simulations is also high, 5.2 m/s, there is 

little agreement between the simulations and the observations, with an MAE of 5.9 m/s, an MDE 

of 5.9 m/s, and an RMSE of 6.7 m/s. In particular, the distinct shift from a high-wind-speed 

regime in the first three days of the observations to a lower-wind-speed regime in the last four 

days of simulations is not represented in the simulations (Figure 24). This wind speed shift, and 

the physical mechanisms inducing it, could merit closer investigation if a future study seeks to 

understand the inadequacies of the simulations as executed here.

3.4 Diurnal Variability of the Wind Resource

Even a low average wind resource may be useful if the variation in the wind resource is such that 

turbines can generate power at times of high demand (Hart et al. 2012). Spectral analysis (not 

shown) of the lidar observations does not suggest a strong diurnal cycle in any of the seasons 

analyzed here. Rather, the synoptic signal dominates variability at the location of the lidar 

observations. A very slight average diurnal signal is seen in the springtime period (Figure 25), 

with the maximum wind speeds occurring in the evening [13:00 Coordinated Universal Time 

(UTC) or 18:30 India Standard Time (IST)], late at night (20:00 UTC or 01:30 IST), and early in 

the morning (1:00 UTC or 6:30 India Standard Time). More observations would be necessary to 

explore whether the Uttarakhand wind resource variability matches the demand of the New Delhi 

population center.

3.5 Comparison of Simulations With Existing Wind Resource 

Assessments for Uttarakhand

The existing wind resource assessment map for Uttarakhand is available from CWET (2013). In 

aggregate, approximately 534 MW of resource is considered to be available, although 

measurements are not available to validate that estimate. As the area of Uttarakhand is 53,484 

km2

, the CWET estimate assumes a very low wind density of 0.01 MW/km2. A direct 

comparison between that estimate and the seasonal simulations executed here is not possible, but 

the aggregated wind speeds predicted with downscaled WRF are low. The November season 

simulations here, with the highest values, predict a domain-average wind speed of less than 2 

m/s

-1

. Converting the wind speeds of the entire domain to power, using the power curve of a GE 

1.5-MW turbine and assuming a density of one turbine per square kilometer (a relatively coarse 

spacing of approximately 12.5 rotor diameters between turbines) suggests a density of 0.03 

MW/km2

, not accounting for exclusion zones or variability in the wind resource outside of the 

time period simulated here. Even this rough estimate, based on simulations that likely

underestimate the wind resource, is higher than the current estimate of wind resources in 

Uttarakhand, and suggests that a modest measurement campaign would be warranted to better 

quantify the possible wind resource.

Of note, the lidar observations suggest wind speeds consistently higher than those predicted by 

the simulations. Without more observation locations, it is not possible to speculate on the broad 

availability of the wind resource in Uttarakhand, but a more extensive wind resource 

measurement campaign would be justified on the basis of the lidar observations alone.
27

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 24: Time series of Doppler lidar winds compared with WRF forecasts from all domains 

during March simulation period

Figure 25: Average diurnal cycle of hub-height winds from lidar observations during the March 

simulation period
28

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

4 Summary and Conclusions

The wind resource in Uttarakhand, India, has been considered to be very low, while the 

proximity of this region to the large population center of New Delhi implies that modest 

investments in transmission could enable any wind resources in Uttarakhand to address ongoing 

energy demands in New Delhi. Very limited observations of the wind resource in Uttarakhand 

have been carried out, however, a comprehensive wind resource assessment campaign would 

require observations from multiple locations within the Uttarakhand region. In lieu of a broad 

measurement campaign, an investigation of possible wind resources in the Uttarakhand region of 

India has been executed based on dynamically-downscaled simulations with the WRF model. 

These simulations have been compared with observations from a profiling Doppler lidar 

deployed by the DOE ARM Program during the Ganges Valley Aerosol Experiment.

Boundary conditions from the Global Forecasting System (1/2 degree in latitude/longitude 

resolution) were dynamically downscaled with the WRF model at very high resolution in the 

boundary layer, with thirteen levels in the lowest 300 m to resolve terrain-driven flow. All four 

domains were centered on the lidar measurement location. Approximately seven days in each of 

the three different seasons were selected for initial simulations to explore the viability of 

dynamic downscaling for wind resource assessment in the complex terrain of the Uttarakhand 

region. These time periods were chosen to sample seasonal variability and for comparison with 

the GVAX Doppler lidar measurements.

Wind variability over approximately seven days within each of the three different seasons 

(August 2011, November 2011, and March 2012) has been quantified. The lidar observations 

during the selected time periods suggest at least modest wind resources, with averages exceeding 

6 m/s at nominally 80 m above the surface. The simulations predicted much lower wind speeds: 

none of the time periods simulated showed evidence of average wind speeds higher than 5 m/s at 

altitudes nominally 80 m above the surface, indicating that the simulations fail to capture the 

physical mechanisms giving rise to the winds observed by the lidar. Increasing the horizontal 

resolution of the simulations and increasing the resolution of the terrain represented in the 

simulations does not improve the agreement between the simulations and the observations. 

Traditional model evaluation metrics, such as mean absolute error, median absolute error, and 

root mean square error, suggest these simulations have poor agreement with the observations. A 

detailed comparison would involve comparing the wind speed probability distribution functions 

over a longer period of time. It appears that although the timing is off, the simulations do have 

some value in simulating the wind speed variability (e.g., Figure 24).

Because the lidar observations indicate that at least one location in Uttarakhand enjoys some 

wind resources, with a slight diurnal cycle that would provide more power at night, a more 

extensive measurement campaign in this region could be appropriate. A set of reliable 

meteorological data sources can be assimilated into future simulations which may incorporate a 

larger domain and capture the relevant mechanisms driving the flow in Uttarakhand. Recently 

developed hybrid downscaling techniques, which combine the benefit of statistical and 

dynamical approaches, could provide accurate multi-year wind resource assessments and reliable 

quantification of their uncertainty at locations where observations are available for several 

months and the deterministic numerical weather prediction model is run over the multi-year 

period.
29

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Acknowledgements

The authors gratefully acknowledge the data visualization efforts of Jennifer Melius and Donna 

Heimiller of National Renewable Energy Laboratory’s (NREL’s) Data Analysis and 

Visualization Group in the Strategic Energy Analysis Center. The WRF simulations were 

completed using NREL's high-performance computing (HPC) system Redmesa for March 2012 

runs, while using NREL's Petascale HPC system Peregrine for August and October/November 

2011 runs. The Redmesa used capabilities of the NREL’s Computational Science Center, which 

is supported by the U.S. Department of Energy (DOE) under Contract No. DE-AC36-

08GO28308. The research performed on the Peregrine system was done using computational 

resources sponsored by DOE at NREL. This research was supported by the Office of Biological 

and Environmental Research of the U.S. Department of Energy as part of the Atmospheric 

Radiation Measurement Climate Research Facility.

This work was funded by DOE and the U.S. Department of State, under the U.S.-India Energy 

Dialogue’s New Technology and Renewable Energy Working Group. The authors would also 

like to acknowledge Michael Mills and Elena Berger of DOE for supporting this research and

express appreciation to Dr. Luca Delle Monache and Dr. Andrew Monaghan of the National 

Center for Atmospheric Research for their review of this report. 
30

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

References

Banta R.M.; Newsom, R.K.; Lundquist, J.K.; Pichugina, Y.L.; Coulter, R.L.; and Mahrt, L.D. 

(2002) “Nocturnal Low-Level Jet Characteristics Over Kansas during CASES-99,” BoundaryLayer Meteorology 105, 221–252.

Browning, K. and Wexler, R. (1968) “The Determination of Kinematic Properties of a Wind 

Field using Doppler Radar,” Journal of Applied Meteorology 7, 105–113.

Brower, M. C., (2012) Wind Resource Assessment: A Practical Guide to Developing a Wind 

Project. Wiley, 296 pages. 

Castro, C. L., Pielke Sr., R. A. and Leoncini, G. (2005). Dynamical downscaling: Assessment of 

value retained and added using the Regional Atmospheric Modeling System (RAMS). Journal of 

Geophysical Research, 110, D05108, doi:10.1029/2004JD004721.

Clifton, A.; Daniels, M. H.; and Lehning, M. (2013) “Effect of Winds in a Mountain Pass on 

Turbine Performance.” Wind Energy. doi: 10.1002/we.1650.

CWET. (2013). Estimation of Installable Wind Power Potential in India at 80 

m. http://www.cwet.tn.nic.in/html/departments_ewpp.html Accessed 17 Dec 2013.

Delle Monache, L.; Eckel, T.; Rife, D.; and Nagarajan, B. (2013) “Probabilistic Weather 

Prediction with an Analog Ensemble,” Monthly Weather Review, 141, 3498-3516.

Dimri, A.; Yasunari, P.T.; Wiltshire, A.; Kumar, P.; Mathison, C.; Ridley, J.; Jacob, D. 2014 

“Application of Regional Climate Models to the Indian Winter Monsoon Over the Western 

Himalayas, Science of The Total Environment, Available online 11 February 2013, ISSN 0048-

9697. http://dx.doi.org/10.1016/j.scitotenv.2013.01.040.

(http://www.sciencedirect.com/science/article/pii/S0048969713000594)

DuVivier, A. K. and Cassano, J. J. (2013) “Evaluation of WRF Model Resolution on Simulated 

Mesoscale Winds and Surface Fluxes near Greenland.” Monthly Weather Review, 141, 941–963. 

doi: http://dx.doi.org/10.1175/MWR-D-12-00091.1

Giebel, G. (2001) On the Benefits of Distributed Generation of Wind Energy in Europe. PhD 

thesis from the Carl von Ossietzky Universität Oldenburg. Fortschr.-Ber. VDI Reihe 6 Nr. 444. 

Düsseldorf, VDI Verlag 2001. ISBN 3-18-344406-2.

Hart, E. K., Stoutenburg, E. D.; Jacobson, M. Z. (2012) “The Potential of Intermittent 

Renewables to Meet Electric Power Demand: Current Methods and Emerging Analytical 

Techniques.” Proceedings of the IEEE, 100, 322-344. doi:10.1109/JPROC.2011.2144951.

Hossain, J.; Sinha, V.; Kishore, V. V. N. (2011) A GIS-Based Assessment of Potential for Wind

Farms in India,” Renewable Energy 36, 3257-3267.
31

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Hughes, M.; Neiman, P. J.; Sukovich, E.; and Ralph, M. (2012)” Representation of the Sierra 

Barrier Jet in 11 Years of a High-Resolution Dynamical Reanalysis Downscaling Compared with 

Long-Term Wind Profiler Observations,” J. Geophys. Res., 117, D18116, 

doi:10.1029/2012JD017869.

Landberg, L. and Watson, S.J. (1994) “Short-term Prediction of Local Wind Conditions,”

Boundary-Layer Meteorology 70, 171.

Mahoney, B.; Parks, K.; Wiener, G.; Liu, Y.; Myers, W.; Sun, J.; Delle Monache, L.; Hopson,

T.; Johnson, D.; Haupt, S. (2012) “A Wind Power Forecasting System to Optimize Grid 

Integration,” IEEE Transactions on Sustainable Energy, 3, 670-682.

Ministry of New and Renewable Energy (2013). http://www.mnre.gov.in/mission-and-vision2/achievements/ Retrieved on 17 Dec 2013.

Nakanishi, M. and Niino, H. (2006) “An Improved Mellor-Yamada Level-3 Model: Its 

Numerical Stability and Application to a Regional Prediction of Advection Fog.” BoundaryLayer Meteorology, 119, 39-407.

Newsom, R., (2012). The DL Handbook. Available 

at http://www.arm.gov/publications/tech_reports/handbooks/dl_handbook.pdf , DOE/SC-ARMTR-101.

Rife, D.L.; Vanvyve, E; Pinto, J.O.; Monaghan, A.J.; Davis, C.A.; Poulos, G.S. (2013)

“Selecting Representative Days for More Efficient Dynamical Climate Downscaling: 

Application to Wind Energy.” J. Appl. Meteor. Climatol., 52, 47–63. 

doi: http://dx.doi.org/10.1175/JAMC-D-12-016.1

Skamarock, W. C., et al. (2008) “A Description of the Advanced Research WRF Version 3.”

NCAR Tech. Note NCAR/TN-475+STR, Mesoscale and Microscale Meteorology Division, 

NCAR, 125 pp. Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.

Unidata/University Corporation for Atmospheric Research, National Centers for Environmental 

Prediction/National Weather Service/NOAA/U.S. Department of Commerce, and European 

Centre for Medium-Range Weather Forecasts, 2003: Historical Unidata Internet Data 

Distribution (IDD) Gridded Model Data, December 2002 - current. Research Data Archive at the 

National Center for Atmospheric Research, Computational and Information Systems Laboratory, 

Boulder, CO. [Available online at http://rda.ucar.edu/datasets/ds335.0.] Accessed§ 01 Oct 2013.

Warner, T. T. (2011). Numerical Weather and Climate Prediction. Cambridge University Press, 

548 pp.

Whiteman, D. (2000). Mountain Meteorology: Fundamentals and Applications. Oxford 

University Press. 355 pp.